BioHarness: Substrate-Aware Evidence Assembly for Biomedical Question Answering across Literature, Knowledge Bases, and Biological Atlases

Clicks: 5
ID: 333645
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #957 of 973 articles by views in BMC Bioinformatics

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 973 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Abstract Motivation Biomedical question answering often requires evidence beyond topically retrieved literature, including gene alias resolution, database identifier normalization, and atlas-derived biological measurements. However, existing retrieval-augmented generation (RAG) systems typically follow a fixed workflow and lack an explicit mechanism for deciding when retrieved text is sufficient, when biomedical entity mapping is needed, or when executable evidence assembly over structured measurements should be invoked. This motivates a substrate-aware large language model (LLM) harness that selectively assembles sufficient evidence by combining literature retrieval, biomedical entity mapping, and access to atlas-derived measurements. Results We introduce BioHarness, an LLM harness for staged biomedical evidence assembly across literature retrieval, biomedical entity-mapping resources, and atlas-derived structured measurements. Across 22,123 biomedical QA items spanning seven answer formats, BioHarness improves the pooled score from 62.2 to 67.2 over the strongest baseline. Ablations, case studies, and backbone-scaling analyses show that these gains arise from repairing evidence-substrate mismatches through reranking, entity grounding, and structured measurement access, rather than from indiscriminately invoking more reasoning steps, retrieving additional literature, or relying on a particular answer-model scale. Comparisons with the frontier models and harness show that BioHarness, despite using an open-weight backbone with only 3B active parameters, approaches frontier-level performance on selected literature QA tasks and outperforms frontier harnesses without web access on biomedical identifier and locus lookup.
Reference Key
openalex_W7165396959 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Meng Xiao, Chuan Qin, Jinmiao Chen, Yihang Cheng, Yuanchun Zhou, Hao Zhu
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag795
URL
https://doi.org/10.1093/bioinformatics/btag795
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.